Against the backdrop of the accelerating global energy transition toward clean and low-carbon sources, hydrogen energy is emerging as a vital component of future energy systems due to its zero-carbon emissions, high energy density, and renewable nature. The safe and efficient transportation of hydrogen is a critical link in the hydrogen energy industry chain. As core connecting components in hydrogen transmission systems, the sealing integrity, hydrogen embrittlement resistance, and long-term service reliability of hydrogen pipeline joints directly impact the stable operation of entire hydrogen transmission systems and the feasibility of large-scale application. This study systematically reviews the research literature on hydrogen pipeline joints from 2014 to 2025 using bibliometric and knowledge graph analysis methods based on the Web of Science Core Collection database. It constructs co-occurrence networks and clustering graphs of keywords to identify core research themes in this field, including hydrogen embrittlement failure mechanisms, degradation of sealing material properties, structural design optimization of joints, and intelligent monitoring and fault diagnosis. Furthermore, this study highlights existing research gaps in evaluating joints’ long-term service performance, developing low-cost and efficient manufacturing technologies, and verifying reliability under complex operating conditions. This study provides a systematic bibliometric perspective on hydrogen pipeline joint technology development, aiding in identifying research frontiers and technological evolution pathways. It offers theoretical support and decision-making references for the safe construction and standardized development of hydrogen energy infrastructure.

This study investigates methane leakage and diffusion from a buried high-pressure natural gas pipeline (8 MPa, 1000 mm diameter) using CFD simulations with the DES turbulence model. Based on homogeneous and layered soil models, the influences of soil porosity (0.46 to 0.54), particle size (10 μm to 100 μm), and soil stratification on the spatial and temporal characteristics of methane diffusion are systematically explored. The simulation results show that (1) methane diffuses from the leak hole to the surrounding soil in an ellipsoidal pattern, with the fastest diffusion speed along the pipeline’s axial direction. (2) In homogeneous soil, within the range of soil parameter values considered in this study, the absolute changes in risk assessment indices (FDR, GDR) caused by soil particle size were more significant; whereas the relative percentage changes in risk assessment indicators caused by soil porosity were more pronounced. (3) In layered soil, the permeability contrast between adjacent layers creates the permeability discontinuity interface effect. When a fine-grained or low-porosity layer overlies a coarse-grained layer, the upper layer acts as a hydraulic barrier, prolonging FDT from 130 s to 354 s while promoting significant horizontal spread at the interface. Conversely, a coarse-grained or high-porosity upper layer accelerates vertical breakthrough. These findings provide a scientific basis for risk assessment, monitoring site optimization, and emergency response planning, particularly in regions with heterogeneous stratified soils.

Understanding the law of gas diffusion in soil is essential for pipeline risk assessment. A 3D CFD model of natural gas leakage and diffusion from buried pipelines was developed in Ansys Fluent using the Detached Eddy Simulation turbulence model, with soil as an isotropic porous medium. Six parameters—burial depth, pipe diameter, transportation pressure, leak hole diameter, hole shape, and hole orientation—were systematically investigated under an identical numerical framework. First Danger Time (FDT), Farthest Danger Range (FDR), and Ground Danger Range (GDR) were used as unified hazard metrics for quantitative cross-factor comparison. As burial depth increases, FDT and FDR increase monotonically, whereas GDR peaks at 2.0 m. Increasing pipe diameter reduces FDT, FDR, and GDR. Higher pipeline pressure shortens FDT and expands FDR and GDR. Leak hole diameter dominates: increasing from 5 to 200 mm reduces FDT to nearly zero. For equal cross-sectional area, rectangular slits yield the highest mass flow rate and largest danger range, while triangular holes produce the shortest FDT owing to strong corner-induced turbulence. Upward leaks give the shortest FDT but smallest FDR; downward leaks produce the largest FDR; side leaks cause the longest FDT but largest GDR. Factor influence ranking by FDT and FDR change is: leak hole diameter, burial depth, pipeline pressure, hole orientation, pipe diameter, and hole shape. These results provide a quantitative framework for hazard assessment, sensor layout optimization, and emergency-response prioritization in buried natural gas pipelines.

Buried natural gas pipeline leakage poses significant risks to public safety and environmental sustainability. A three-dimensional transient computational fluid dynamics model is established to investigate gas leakage and diffusion behavior, with a focus on quantifying the differences between standalone soil models and soil–atmosphere-coupled models. First danger time (FDT), farthest danger range (FDR), and ground danger range (GDR) are adopted as standardized safety metrics that translate complex concentration fields into actionable emergency response parameters. Simulation results indicate that the coupled model predicts an FDT 4.9% earlier and a GDR 39.7% smaller than the standalone soil model under no-wind conditions, highlighting the necessity of coupling. Increasing temperature reduces leakage mass flow rate by 8.4% but produces only marginal changes in FDT (6.01–6.47 s) and GDR (1.888–1.973 m) across the −10 °C to 40 °C envelope. Wind speed exhibits a non-monotonic, time-dependent effect: the shortest FDT occurs at 5 m/s (5.71 s), while the worst-case spatial hazard occurs at 2 m/s (FDR = 3.423 m at 60 s, 3.304 m at 100 s). A crosswind configuration reduces GDR by merely 1.6% when compared with an along-wind configuration, indicating weak directional sensitivity. Topography dominates hazard onset timing: a canyon shortens FDT to 0.33 s, while a hill delays it to 18.48 s—a 43-fold span. Offset obstacles with upwind configuration maximize FDT delay to 8.07 s (+30.6%) while enabling late-stage wake-driven GDR expansion. These findings provide quantitative guidance for risk assessment, emergency response, and monitoring layout of buried natural gas pipelines.

Accurate prediction of natural gas dispersion from buried pipelines is critical for risk assessment and emergency response. However, conventional numerical simulations often simplify soil as a homogeneous isotropic porous medium, which deviates significantly from real-world conditions characterized by stratification, anisotropy, and spatial heterogeneity. This study systematically investigates the effects of these non-ideal soil characteristics on methane diffusion behavior using computational fluid dynamics (CFD). Four distinct soil models—a baseline homogeneous model, a layered model, an anisotropic model, and a spatially heterogeneous model—were constructed and compared under identical leakage scenarios. Key risk indicators, including First Danger Time (FDT), Farthest Danger Range (FDR), Ground Danger Range (GDR), and leakage mass flow rate, were quantitatively evaluated. Results indicate that soil layering enhances vertical migration and expands horizontal hazard ranges, reducing FDT by approximately 8%. Anisotropy introduces a pronounced directional dependence in gas migration, with horizontal-preferred permeability leading to severe underestimation of lateral risk by homogeneous assumptions. The spatially heterogeneous model exhibits reduced hazard ranges compared to the homogeneous case but accelerates early breakthrough. Comprehensive evaluation reveals that the homogeneous model systematically underestimates lateral diffusion distances and delays alarm times. This study provides a quantitative basis for selecting appropriate soil modeling strategies, emphasizing that incorporating soil heterogeneity is essential for reliable safety assessments of buried gas pipelines.

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems.